VSC-ACGAN: Bearing Fault Diagnosis Model Applied to Imbalanced Samples

Abstract The rapid development of deep learning has promoted the application of rolling bearing fault diagnosis techniques. However, in practical applications, the researchers often faces the challenge of a serious imbalance in the proportion of normal and fault states. This imbalance greatly affects the accuracy of diagnosis. Therefore, this paper proposes a novel fault diagnosis framework based on an auxiliary classifier generative adversarial network (ACGAN). Firstly, the stacked contractive autoencoder (SCAE) is cleverly combined with the discriminator network to improve its feature extraction capability and fault diagnosis accuracy. Subsequently, the original algorithm's focus on different types of samples is optimised to improve the generalisation of the diagnostic network. Finally, the stability of the generator network training is optimised with the help of the metric properties of the Kullback-Leibler (KL) scatter, and thus the variational stacked contractive-ACGAN(VSC-ACGAN) model is proposed. The experimental results show that the fault diagnosis accuracy reaches 99.75 with 200 training samples of each class on the Case Western Ruthenian University (CWRU) bearing dataset, which is significantly better than other algorithms. Under the same conditions, on the Jiangnan University (JNU) bearing dataset, the accuracy reaches 99.25, which shows good generalization and provides a broad prospect for future applications.